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You're reading from  Generative Adversarial Networks Cookbook

Product typeBook
Published inDec 2018
PublisherPackt
ISBN-139781789139907
Edition1st Edition
Concepts
Artificial Intelligence
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Josh Kalin
Josh Kalin
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Josh Kalin

Josh Kalin is a Physicist and Technologist focused on the intersection of robotics and machine learning. Josh works on advanced sensors, industrial robotics, machine learning, and automated vehicle research projects. Josh holds degrees in Physics, Mechanical Engineering, and Computer Science. In his free time, he enjoys working on cars (has owned 36 vehicles and counting), building computers, and learning new techniques in robotics and machine learning (like writing this book).
Read more about Josh Kalin

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Exercise

This is the exercise set for the Chapter:

  1. Expand the generator to allow for the use of the model's flexibility. Experiment with different parameters for those two variables in the model definition. Describe the effects.
  2. Expand the training class capability to be able to take in different datasets other than MNIST. Do you need to change anything else, but allow for the training data to be an argument? What else needs to be changed?
  3. Create a graphical way to visualize the Discriminator and Generator losses with loss as your y value and epochs as your x value.
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Generative Adversarial Networks Cookbook
Published in: Dec 2018Publisher: PacktISBN-13: 9781789139907
© 2018 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Josh Kalin

Josh Kalin is a Physicist and Technologist focused on the intersection of robotics and machine learning. Josh works on advanced sensors, industrial robotics, machine learning, and automated vehicle research projects. Josh holds degrees in Physics, Mechanical Engineering, and Computer Science. In his free time, he enjoys working on cars (has owned 36 vehicles and counting), building computers, and learning new techniques in robotics and machine learning (like writing this book).
Read more about Josh Kalin

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